|
|
|
|
|
|
| Study on Hyperspectral Quantitative Inversion of Ionic Rare Earth Ores |
| CHENG Gong1,2, LI Jia-xuan1,2, WANG Chao-peng3, HU Zhen-guang3, NING Qing-kun3 |
1. Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring（Central South University), Ministry of Education, Changsha 410083, China
2. School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
3. Chinalco Guangxi Zhuang Chongzuo Rare Earth Development Co., Ltd., Chongzuo 532299, China |
|
|
|
|
Abstract Spectral absorption characteristics are used in the analysis of soil, mineral and plant material composition with spectral measurement technology, which is a hyperspectral remote sensing technology developed in recent years. It has many advantages, such as fast speed, high efficiency, low cost, low loss and so on. Rare earth is a strategic element with rich and unique physical and chemical properties, such as magnetic, optical, electrical, etc., which is widely used in aerospace, electronics, petrochemical, metallurgy, machinery, energy, agriculture and other fields. It is indispensable strategic material for the development of high-tech and cutting-edge national defense technology and the transformation of traditional industries in the world today. In recent years, As a result of the increasing demand and the enhancing value of rare earth resources, it has become an important research area to discover how to detect rare earth resources rapidly in large area and implement rare earth mining properly. Through spectral collection and analysis of rare earth minerals, this study carried out a series of researches on the correlation between rare earth elements and their chemical features. During the study, 12 rare earth mineral samples were collected from Liutang rare earth ore area of Chongzuo City, Guangxi, and the corresponding reflectance spectrum data were measured by using SVC HR1024I portable ground object wave spectrometer in laboratory. Continuous dispatch is implemented for the measured spectral features of the samples, and relative absorption analysis is carried out for prominent diagnostic absorption wavelength. Thus the linear relationship between the spectrum and the total content of rare earth elements and the contents of rare earth elements in ore samples was established according to its spectral characteristics. The results reveal that the five characteristic absorption bands of rare earth elements are 370, 950, 1 400, 1 900 and 2 200 nm in visible light and near infrared, respectively. The intensity of the five absorption band is related to the total rare earth content linearly, with R2 reaching 0.69, also discovered that the correlation between rare earth content and the visible light band is larger, and the correlation analysis between the visible light wave band and the total rare earth content of the sample was carried out. The 10 bands which have the strongest correlation with the total content of rare earth are 340, 350, 360, 370, 390, 400, 420, 480, 550 and 760 nm, respectively. The linear regression method is used to get the prediction model of the reflectance value and the total sample content of visible bands with high accuracy with R2 greater than 0.95. Also linear modeling is established by using the visible light wave band and 15 rare earth elements content values, with the correlation coefficient may reach above 0.9, which also shows that each single rare earth element has a strong correlation with the visible light wave region. By studying the spectral characteristics and chemical analysis of rare earth mineral samples, a linear regression analysis was carried out for 5 diagnostic absorption wavelengths and the visible band and the total rare earth element content of the samples, and for 15 kinds of rare earth element contents. The quantitative evaluation model of rare earth content in ore samples is established, which has certain reference value for rapid quantification-semi-quantitative evaluation of rare earth ores, and lays a theoretical foundation for extracting mineral information from hyperspectral remote sensing of rare earth ores and elements. It provides a scientific and effective theoretical basis for the ultimate realization of efficient exploitation of rare earth resources, reducing consumption and production costs at the source, reducing environmental damage and pollution, and promoting the strategic development and utilization of medium-heavy rare earth resources.
|
|
Received: 2018-03-01
Accepted: 2018-08-20
|
|
|
|
[1] WANG Deng-hong，ZHAO Zhi，YU Yang，et al（王登红, 赵 芝, 于 扬, 等）. Acta Geoscientica Sinica（地球学报），2017, 38(3): 317.
[2] HE Yao，CHENG Liu，LI Yi（何 耀，程 柳，李 毅）. Chinese Rare Earths（稀土）, 2015, 36(4): 98.
[3] Meer F V D. International Journal of Applied Earth Observation & Geoinformation, 2004, 5(1): 55.
[4] Turner D J, Rivard B, Groat L A. American Mineralogist, 2016, 101(10): 2264.
[5] Van Der Meer F D. International Journal of Applied Earth Observation and Geoinformation, 2004, 5(1): 55.
[6] Ardanova L I, Getman E I, Loboda S N, et al. Inorganic Chemistry, 2010, 49: 10687.
[7] Dai J, Wang D, Wang R, et al. Journal of Applied Remote Sensing, 2013, 7(1): 073513.
[8] ZHANG Qin-yu，ZHANG Deng-rong，HUANG Guo-cheng，et al（章钦瑜, 张登荣, 黄国成, 等）. Remote Sensing for Land & Resources（国土资源遥感）, 2012, (1): 120.
[9] DAI Jing-jing，WANG Deng-hong，CHEN Zhen-hui，et al（代晶晶, 王登红, 陈郑辉, 等）. Acta Geoscientica Sinica（地球学报）, 2013，(3): 354.
[10] ZHAO Heng-qian，ZHAO Xue-sheng，CEN Yi，et al（赵恒谦, 赵学胜, 岑 奕, 等）. Spectroscopy and Spectral Analysis（光谱学与光谱分析）, 2017, 37(3): 869.
[11] ZHAO Ting，WANG Deng-hong，WANG Zhao-zhao，et al（赵 汀, 王登红, 王钊钊, 等）. Mineral Deposits（矿床地质）, 2014，(s1): 1205. |
| [1] |
WANG Cai-ling1，ZHANG Jing1，WANG Hong-wei2*, SONG Xiao-nan1, JI Tong3. A Hyperspectral Image Classification Model Based on Band Clustering and Multi-Scale Structure Feature Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 258-265. |
| [2] |
GAO Hong-sheng1, GUO Zhi-qiang1*, ZENG Yun-liu2, DING Gang2, WANG Xiao-yao2, LI Li3. Early Classification and Detection of Kiwifruit Soft Rot Based on
Hyperspectral Image Band Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 241-249. |
| [3] |
WU Hu-lin1, DENG Xian-ming1*, ZHANG Tian-cai1, LI Zhong-sheng1, CEN Yi2, WANG Jia-hui1, XIONG Jie1, CHEN Zhi-hua1, LIN Mu-chun1. A Revised Target Detection Algorithm Based on Feature Separation Model of Target and Background for Hyperspectral Imagery[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 283-291. |
| [4] |
CHU Bing-quan1, 2, LI Cheng-feng1, DING Li3, GUO Zheng-yan1, WANG Shi-yu1, SUN Wei-jie1, JIN Wei-yi1, HE Yong2*. Nondestructive and Rapid Determination of Carbohydrate and Protein in T. obliquus Based on Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3732-3741. |
| [5] |
HUANG You-ju1, TIAN Yi-chao2, 3*, ZHANG Qiang2, TAO Jin2, ZHANG Ya-li2, YANG Yong-wei2, LIN Jun-liang2. Estimation of Aboveground Biomass of Mangroves in Maowei Sea of Beibu Gulf Based on ZY-1-02D Satellite Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3906-3915. |
| [6] |
ZHOU Bei-bei1, LI Heng-kai1*, LONG Bei-ping2. Variation Analysis of Spectral Characteristics of Reclaimed Vegetation in an Ionic Rare Earth Mining Area[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3946-3954. |
| [7] |
YUAN Wei-dong1, 2, JU Hao2, JIANG Hong-zhe1, 2, LI Xing-peng2, ZHOU Hong-ping1, 2*, SUN Meng-meng1, 2. Classification of Different Maturity Stages of Camellia Oleifera Fruit
Using Hyperspectral Imaging Technique[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3419-3426. |
| [8] |
FU Gen-shen1, LÜ Hai-yan1, YAN Li-peng1, HUANG Qing-feng1, CHENG Hai-feng2, WANG Xin-wen3, QIAN Wen-qi1, GAO Xiang4, TANG Xue-hai1*. A C/N Ratio Estimation Model of Camellia Oleifera Leaves Based on
Canopy Hyperspectral Characteristics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3404-3411. |
| [9] |
SHEN Ying, WU Pan, HUANG Feng*, GUO Cui-xia. Identification of Species and Concentration Measurement of Microalgae Based on Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3629-3636. |
| [10] |
XIE Peng, WANG Zheng-hai*, XIAO Bei, CAO Hai-ling, HUANG Yi, SU Wen-lin. Hyperspectral Quantitative Inversion of Soil Selenium Content Based on sCARS-PSO-SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3599-3606. |
| [11] |
QIAN Rui1, XU Wei-heng2, 3 , 4*, HUANG Shao-dong2, WANG Lei-guang2, 3, 4, LU Ning2, OU Guang-long1. Tea Plantations Extraction Based on GF-5 Hyperspectral Remote Sensing
Imagery in the Mountainous Area[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3591-3598. |
| [12] |
ZHU Zhi-cheng1, WU Yong-feng2*, MA Jun-cheng2, JI Lin2, LIU Bin-hui3*, JIN Hai-liang1*. Response of Winter Wheat Canopy Spectra to Chlorophyll Changes Under Water Stress Based on Unmanned Aerial Vehicle Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3524-3534. |
| [13] |
YANG Lei1, 2, 3, ZHOU Jin-song1, 2, 3, JING Juan-juan1, 2, 3, NIE Bo-yang1, 3*. Non-Uniformity Correction Method for Splicing Hyperspectral Imager Based on Overlapping Field of View[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3582-3590. |
| [14] |
SUN Lin1, BI Wei-hong1, LIU Tong1, WU Jia-qing1, ZHANG Bao-jun1, FU Guang-wei1, JIN Wa1, WANG Bing2, FU Xing-hu1*. Identification Algorithm of Green Algae Using Airborne Hyperspectral and Machine Learning Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3637-3643. |
| [15] |
TAO Jing-zhe1, 3, SONG De-rui1, 3, SONG Chuan-ming2, WANG Xiang-hai1, 2*. Multi-Band Remote Sensing Image Sharpening: A Survey[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 2999-3008. |
|
|
|
|
